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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """export checkpoint file into air, onnx, mindir models"""
- import argparse
- import numpy as np
-
- import mindspore as ms
- import mindspore.common.dtype as mstype
- from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
-
- from src.CascadeRcnn.cascade_rcnn_r101 import CascadeRcnn_Infer
- from src.config import config
-
- parser = argparse.ArgumentParser(description='cascadercnn_export')
- parser.add_argument("--device_id", type=int, default=0, help="Device id")
- parser.add_argument("--file_name", type=str, default="cascade_rcnn", help="output file name.")
- parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"], default="MINDIR", help="file format")
- parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
- help="device target")
- parser.add_argument('--ckpt_file', type=str, default='', help='cascadercnn ckpt file.')
- args = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
- if args.device_target == "Ascend":
- context.set_context(device_id=args.device_id)
-
- if __name__ == '__main__':
- net = CascadeRcnn_Infer(config=config)
-
- param_dict = load_checkpoint(args.ckpt_file)
-
- param_dict_new = {}
- for key, value in param_dict.items():
- param_dict_new["network." + key] = value
-
- load_param_into_net(net, param_dict_new)
-
- device_type = "Ascend" if context.get_context("device_target") == "Ascend" else "Others"
- if device_type == "Ascend":
- net.to_float(mstype.float16)
-
- img = Tensor(np.random.rand(config.test_batch_size, 3, config.img_height, config.img_width), ms.float32)
- img_metas = Tensor(np.random.uniform(0.0, 1.0, size=[config.test_batch_size, 4]), ms.float32)
- input_tensor = [img, img_metas]
- export(net, *input_tensor, file_name=args.file_name, file_format=args.file_format)
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